Curriculum details

Data is widely considered the world’s ‘new oil’, and data science is a powerful discipline that enables organisations to extract actionable insights from large volumes of data. This unit introduces students to the core concepts and practices of data science using Python — a widely adopted programming language known for its clarity, flexibility, and strong support for data analysis.

Students will first build a solid foundation in Python programming, including data types, data structures, control structures, functions, and file operations. They will then progress to using Python’s data science libraries — such as NumPy, Pandas, and Matplotlib — for tasks such as data wrangling, exploration, and visualisation. Real-world datasets from areas such as health, business, sustainability, and community services will be used to provide practical and inclusive context, ensuring representation of diverse perspectives and experiences.

The unit emphasises ethical data handling, reproducibility, and responsible analysis, while fostering critical thinking and hands-on experience. It also aligns with other units in the MINFTN program, providing foundational programming and data analysis skills that support more advanced study in areas such as machine learning, data mining, and business intelligence. The aim of this unit is to equip students with foundational knowledge and practical skills in Python and data science, enabling them to contribute to societal benefit and the common good.

Databases are a critical technology for organisations, enabling the efficient recording, processing, and management of data while maintaining data quality and security. This unit equips students with foundational knowledge and practical skills in database design and implementation. Students will develop the ability to identify and model organisational information requirements, construct conceptual data models, verify structural integrity through normalisation techniques, and select appropriate database technologies to meet organisational needs.

The unit introduces both relational and non‑relational data storage approaches and associated toolsets. Students will learn to implement and use relational database technologies and Structured Query Language (SQL) in a cloud environment using Microsoft Azure. The unit also covers core database administration tasks and ethical considerations related to the management and protection of organisational data. Designed in alignment with Microsoft’s curriculum, the unit provides a pathway to the DP‑900: Microsoft Azure Data Fundamentals certification.

The aim of this unit is to prepare students to select, design, implement, and administer large‑scale database solutions that address organisational data and information management requirements while upholding data privacy, security, and the dignity of the individuals served by the organisation.

Machine learning involves enabling computational systems to learn from data and perform a wide range of tasks without being explicitly programmed. These models and algorithms underpin many aspects of contemporary digital life, including email filtering, search engines, social media platforms, virtual assistants, healthcare applications, and recommendation systems.

This unit offers a technical and practice‑oriented introduction to core machine learning models and algorithms. Key topics include pattern discovery, classification, regression, feature extraction, and data visualisation. Students will also engage in hands‑on implementation using Python and the Scikit‑Learn library to address real‑world challenges, including applications in digital health.

The unit further examines issues of machine bias and the potential consequences of inequitable or harmful algorithmic outcomes. Overall, the unit aims to equip students with foundational knowledge and practical skills to design ethical, effective, and responsible.

The explosion in data and digital technologies has opened new ways of obtaining data-driven insights. Organisations increasingly require professionals who can work with modern data ecosystems, streaming data sources, and visualisation tools to extract, analyse, and communicate insights.

This unit introduces advanced techniques in data analytics, including big data processing and visualisation using industry-standard and open-source tools. Students will use platforms such as Power BI, Google Looker Studio, Tableau, and Python-based libraries and big data technologies (e.g., Hadoop, Spark, cloud-native platforms) to transform raw data into meaningful insights. Students will also explore data ethics, geospatial analysis, and advanced dashboarding techniques.

The aim of this unit is to equip students with job-ready skills in applied analytics, big data techniques, data storytelling, and dynamic reporting—enabling them to deliver strategic, ethical, and environmentally responsible decisions in complex real-world scenarios.

Deep learning drives many real-world AI applications, including computer vision, natural language processing, and large language models (LLMs), and is a core approach within modern artificial intelligence (AI). Graduates, therefore, need the capability to design AI projects responsibly, select appropriate methods, and evaluate solutions that are effective, safe, and aligned with the common good.

This unit develops conceptual and practical skills in contemporary AI, with a strong emphasis on deep learning. Students begin with a recap of core machine learning concepts, then progress to designing and developing deep learning models and exploring other emerging AI technologies. They will work through the full model lifecycle, including development, training, inference, testing, and evaluation. Using tools such as Python and Keras, students implement and assess AI solutions across key areas, including computer vision, natural language processing, large language models (LLMs), anomaly detection, and reinforcement learning. The unit also integrates Microsoft Azure AI Fundamentals to familiarise students with industry standards, design principles, and common AI solution patterns. Throughout, students examine foundational AI use cases that promote stewardship and the common good, with sustained attention to ethics, safety, and human dignity.

This unit aims to equip students with advanced AI knowledge and skills to design, develop, and evaluate AI-enabled applications responsibly, promoting the common good and upholding human dignity.

Organisations today face the challenge and opportunity of leveraging vast amounts of data to derive actionable insights. Data mining is a critical process for discovering patterns, relationships, and trends within large and complex datasets to support strategic and operational decision-making.

This unit builds on foundational studies in data science and programming, and provides a comprehensive exploration of the knowledge discovery process, incorporating essential topics such as data pre-processing, classification, clustering, association rule mining, anomaly detection, sentiment analysis, and sequential pattern mining. Emphasis is placed on applying both descriptive and predictive data mining techniques to solve real-world problems, using visual data mining tools (e.g. RapidMiner and Orange) and Python programming.

Case studies and applied projects will help students design, implement, and evaluate end-to-end data mining workflows, applying ethical principles and critically reflecting on issues of data privacy, bias, and explainability.

The primary aim of this unit is to develop industry-ready professionals capable of applying data mining techniques and tools to support informed and ethical decision-making across diverse domains.

The aim of this unit is to enhance knowledge of external environments in which organisations operate and apply this knowledge to activities relevant to the marketing function.
Organisations need to evaluate, analyse and respond to external forces. Key amongst the environments within which organisations operate are legal, political, economic and consumer environments. This unit considers the required knowledge effective managers need for analysing the challenges of the changing external environment by learning strategies to support effective decision-making in Marketing, Business Law and Economics.
This unit also develops an understanding of the potential risks and opportunities of doing business in competitive consumer and business markets. Key legislative and economic frameworks relevant to business, with a particular emphasis on consumer and business law perspectives, are considered. The knowledge applied is relevant to the outwardly focused management function of marketing.